Present delivery environments provide static mechanisms for the scheduling and delivery of goods, or other products or services. In some environments, there are a fixed number of delivery vehicles and personnel, and orders for delivery are accepted based upon the capacity of these vehicles and personnel. For example, a flower shop may have two trucks and two drivers, and, based upon how its schedule fills up on a particular day, the shop may either accept an order or inform the customer that flowers cannot be delivered when desired. One disadvantage of such a system is that customers are disappointed when their needs cannot be met and may not return to the shop for future service. As another example, a pizza parlor may schedule deliveries based entirely upon the availability of a fixed pool of drivers and a history of order traffic. To handle its deliveries, the pizza parlor would typically schedule sufficient delivery trucks and personnel to accommodate an average delivery load for that day, based upon past order data. When the load exceeds the availability of drivers, the delivery times available for the delivery of more orders get pushed out so far that customers are apt to go elsewhere. The pizza parlor thus pays an opportunity cost for not having its delivery schedule able to dynamically handle a changing order environment.
Other environments have offered other solutions. For example, some environments provide load balancing of orders among a fixed set of delivery resources after the orders are made. For example, a beverage distributor might have a fleet of different types of delivery vehicles with various sizes and load capacities. In order to accommodate a variety of different types of customer orders, varying from small sandwich shops, to small convenience stores, then to large chain stores, the distributor needs to optimize deliveries across the fleet. Typically, the distributor performs load balancing by shutting off orders at a particular time of day and running an optimizing computer program to automatically assign particular orders to particular vehicles. The optimizing program uses information about each customer and about each vehicle to maximize the drive time efficiency of the distributor's fleet. One disadvantage of such a system is that orders are typically cut off early during the day because the system is static—it performs post-processing on the order data. Although such a solution optimizes the available resource, it doesn't address accommodating an ever-changing order environment.